Cloud based services leverage continuous integration and deployment, meaning that code changes are being made on a constant basis and those changes are deployed from a developer's computer out to production at a rapid cadence. In this model, any regressions in the code may have to be found as early as possible, preferably at the developer's computer before she or he commits the code. While detection of functional issues is a tenable challenge, detection of poorly performing and scaling code at the developer stage is historically challenging (usually requiring days of lab based analysis or the risk of rolling out code to larger swaths of customers) which may defeat some of the core principals of continuous integration and deployment.
In some approaches, developers put benchmarks in their code receive benchmark measurements, analyze, rewrite, and then repeat the whole process. The static benchmark based approach may not necessarily reflect actual performance hot spots such as exceptions that are associated with higher memory usage or processor usage.